Text Classification
Transformers
Safetensors
qwen3
reward-model
prm
code-security
text-embeddings-inference
Instructions to use AetherPrior/qwen3-8b-impl-prm-exec-think with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AetherPrior/qwen3-8b-impl-prm-exec-think with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="AetherPrior/qwen3-8b-impl-prm-exec-think")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("AetherPrior/qwen3-8b-impl-prm-exec-think") model = AutoModelForSequenceClassification.from_pretrained("AetherPrior/qwen3-8b-impl-prm-exec-think", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,125 Bytes
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license: apache-2.0
base_model: Qwen/Qwen3-8B
tags:
- reward-model
- prm
- code-security
- qwen3
library_name: transformers
pipeline_tag: text-classification
---
# Qwen3-8B Implementation-level PRM (exec-filtered, thinking)
Scalar Bradley–Terry preference model over **implementation guidelines**
(`<high_level>` + `<implementation>`) for secure coding.
## Training
- Base: `Qwen/Qwen3-8B` → `AutoModelForSequenceClassification` (`num_labels=1`)
- Prefs: evolved (t+) / preferred (t) / degraded (t−) guidelines, exec-filtered
with thinking enabled (keep t+ iff func∧sec; t− iff ¬sec)
- Independent pairwise BT: t≻t−, t+≻t−, t+≻t
- Data: combined 5cwe + other CWEs, K=3 paired attempts (attempt-0 + 2 extras),
`preferences_synth_triple_exec_combined_allpairs_k3` (6933 pairs; 6192/741)
- Val accuracy (pairwise): **0.968** (741 pairs)
## Scoring
Chat messages: user = coding task, assistant = guideline trace.
Serve with vLLM pooling/classify and `POST /classify`.
## Citation / project
Internal: `prm_secode_eval/recode-style` `prm_impl_bt_triple_exec_combined_allpairs_k3` best.
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